Evidence map›Paper›PMID 39763217›Full record

ArticleThe Journal of infectious diseases2025

Predictive Markers of Incident Tuberculosis in Close Contacts in Brazil and India.

Betânia M F Nogueira, Francys Rangel, Alice M S Andrade, Evangeline Ann Daniel, Marina C Figueiredo, Cody Staats, Valeria C Rolla, Afrânio L Kritski, Marcelo Cordeiro-Santos, Amita Gupta and 4 more

Abstract read
In one paragraph

Article in The Journal of infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Betânia M F NogueiraPrograma de Pós-graduação em Ciências da Saúde, Universidade Federal da Bahia, Salvador, Brazil.ORCID 0000-0003-4399-4888
Francys RangelMultinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil.ORCID 0000-0001-5315-8168
Alice M S AndradeMultinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil.
Evangeline Ann DanielNational Institute for Research in Tuberculosis, Indian Council of Medical Research (ICMR), Chennai, India.ORCID 0000-0002-7378-717X
Marina C FigueiredoDivision of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Cody StaatsDivision of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.ORCID 0009-0002-9044-6382
Valeria C RollaInstituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Afrânio L KritskiPrograma Acadêmico de Tuberculose da Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
Marcelo Cordeiro-SantosFundação de Medicina Tropical Dr. Heitor Vieira Dourado, Manaus, Brazil.ORCID 0000-0002-7140-7145
Amita GuptaDivision of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0001-7036-2718
Luke Elizabeth HannaNational Institute for Research in Tuberculosis, Indian Council of Medical Research (ICMR), Chennai, India.
Timothy R SterlingDivision of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Mariana Araújo-PereiraMultinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil.ORCID 0000-0002-1141-1580
Bruno B AndradePrograma de Pós-graduação em Ciências da Saúde, Universidade Federal da Bahia, Salvador, Brazil.ORCID 0000-0001-6833-3811

Funding

CCASAnet: Caribbean, Central and South America NetworkU01AI069923 · NIAID · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Pedro Enrique Cahn, Jessica L Castilho · 2006 to 2026
$41.6M
Conselho Nacional de Desenvolvimento Científico e Tecnológico, BrazilDepartamento de Ciência e Tecnologiagovernment of India's Department of BiotechnologyIndian Council of Medical Research 5/8/5/45/Adhoc/2022/ECD-1Intramural Research Program of the Fundação José SilveiraIntramural Research Program of the Fundação Oswaldo CruzNational Institute of Allergy and Infectious Diseases U01-AI069923NIAID NIH HHS U01 AI069923Regional Prospective Observational Research for Tuberculosis India ConsortiumSecretaria de Ciência e Tecnologia, Ministério da Saúde, Brazil 25029.000507/2013-07
6 · The paper itself

Abstract

There are insufficient predictors of progression to tuberculosis among contacts. A case-control study within RePORT-Brazil matched 20 QuantiFERON-positive progressors and 40 nonprogressors by sex, age, and exposure duration. Twentynine cytokines were measured using a Luminex assay with QuantiFERON-TB Gold Plus supernatants collected at baseline and evaluated using machine learning for tuberculosis prediction. The same markers were evaluated in 8 QuantiFERON-positive progressors and 12 nonprogressors from India. Interleukin 8, interleukin 10, and CCL3 levels predicted incident tuberculosis (area under the receiver operating characteristic curve, 0.75) in 2 years with sensitivity and specificity >80%, in both cohorts. This signature predicted tuberculosis progression in close contacts meeting World Health Organization goals.

Indexed as

BiomarkersCytokinesTuberculosisAdolescentAdultBrazilCase-Control StudiesDisease ProgressionFemaleHumansIncidenceIndiaInterleukin-8Machine LearningMaleMiddle AgedBiomarkersCytokinesInterleukin-8biomarker predictionBrazilcytokineIndiamulticentric studyQuantiFERONtuberculosis

Identifiers

PMID39763217
PMCPMC12455298

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.